Collaborative Research: ITR-(ASE+EVS)-(dmc+sim) Data Driven Simulation of the Subsurface: Optimization and Uncertainty Estimation
合作研究:ITR-(ASE EVS)-(dmc sim) 地下数据驱动模拟:优化和不确定性估计
基本信息
- 批准号:0426354
- 负责人:
- 金额:$ 18.6万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2004
- 资助国家:美国
- 起止时间:2004-10-01 至 2008-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Intellectual Merit. Remote sensing is employed in science and engineering problems to infer material properties when these properties can not be directly sampled. To better understand and manage our environment for safety and economic reasons, much progress has been made in imaging the subsurface and estimating physical properties based on remote sensing data. Repeated observations over targets for environmental remediation and reservoir production has become a recognized diagnostic tool for assisting management decisions. In addition, improved optimization techniques capable of responding to large, multi-resolution, disparate, dynamic datasets in a fault tolerant and adaptive fashion are a fundamental requirement for effectively estimating and minimizing the uncertainty in any data driven application. The integrated and e_ective treatment of these issues motivates the present project. The assembled research team proposes to advance the mathematical, engineering and computational foundations necessary to enhance our understanding and extend the predictive capabilities of the physical processes that govern the subsurface phenomena at multiple temporal and spatial scales. Target applications include management of aquifers for water resources, optimizing oil and gas production, and monitoring environmental risks e.g., at waste containment sites or arising from natural hazards.The intellectual merits of the project include: (1) development of the next generation of accurate, multi-scale, coupled chemical, uid, geomechanical, and geophysical simulators for modeling instrumented subsurface environments; (2) large scale optimization techniques (based on a hybridization of global and local approaches) to drive reliable decision-making and a dynamic symbiotic feedback between computation and data; (3) deployment of an autonomic Grid middleware for providing the adequate processing substrate and data management services for (1) and (2). The realization of the above contributions will result in the Data Driven Subsurface Simulation Framework (DDSSF).
智力优势。在科学和工程问题中,当材料的性质不能直接取样时,遥感可以用来推断这些性质。为了更好地了解和管理我们的环境,安全和经济的原因,已经取得了很大的进展,在成像的地下和估计的基础上遥感数据的物理特性。 对环境补救和油藏开采目标的反复观察已成为协助管理决策的公认诊断工具。此外,能够以容错和自适应方式响应大型、多分辨率、不同的动态数据集的改进的优化技术是有效估计和最小化任何数据驱动应用中的不确定性的基本要求。综合有效地处理这些问题是本项目的动机。组装的研究团队提出推进必要的数学,工程和计算基础,以增强我们的理解和扩展在多个时间和空间尺度上管理地下现象的物理过程的预测能力。目标应用包括管理含水层的水资源、优化石油和天然气生产以及监测环境风险,例如,该项目的智力价值包括:(1)开发下一代精确的、多尺度的、耦合的化学、流体、地质力学和地球物理模拟器,用于模拟仪器化的地下环境;(2)大规模优化技术(基于全局和局部方法的混合),以推动可靠的决策以及计算和数据之间的动态共生反馈;(3)部署自主网格中间件,为(1)和(2)提供足够的处理底层和数据管理服务。上述贡献的实现将导致数据驱动的地下模拟框架(DDSSF)。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Manish Parashar其他文献
Event-Driven FaaS Workflows for Enabling IoT Data Processing at the Cloud Edge Continuum
用于在云边缘连续体实现物联网数据处理的事件驱动的 FaaS 工作流程
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Christian Sicari;Daniel Balouek;Manish Parashar;Massimo Villari - 通讯作者:
Massimo Villari
Towards comprehensive dependability-driven resource use and message log-analysis for HPC systems diagnosis
- DOI:
10.1016/j.jpdc.2019.05.013 - 发表时间:
2019-10-01 - 期刊:
- 影响因子:
- 作者:
Edward Chuah;Arshad Jhumka;Samantha Alt;Daniel Balouek-Thomert;James C. Browne;Manish Parashar - 通讯作者:
Manish Parashar
In-situ feature-based objects tracking for data-intensive scientific and enterprise analytics workflows
- DOI:
10.1007/s10586-014-0396-6 - 发表时间:
2014-08-22 - 期刊:
- 影响因子:4.100
- 作者:
Solomon Lasluisa;Fan Zhang;Tong Jin;Ivan Rodero;Hoang Bui;Manish Parashar - 通讯作者:
Manish Parashar
Guest Editorial on Challenges of Large Applications in Distributed Environments (CLADE)
- DOI:
10.1007/s10586-005-4091-5 - 发表时间:
2005-10-01 - 期刊:
- 影响因子:4.100
- 作者:
Ray Bair;Ioana Banicescu;Manish Parashar - 通讯作者:
Manish Parashar
Computing Everywhere, All at Once: Harnessing the Computing Continuum for Science
一次性计算无处不在:利用计算连续体促进科学发展
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Manish Parashar - 通讯作者:
Manish Parashar
Manish Parashar的其他文献
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{{ truncateString('Manish Parashar', 18)}}的其他基金
EAGER: Exploring intelligent services for managing uncertainty under constraints across the Computing Continuum: A case study using the SAGE platform
EAGER:探索在整个计算连续体的约束下管理不确定性的智能服务:使用 SAGE 平台的案例研究
- 批准号:
2238064 - 财政年份:2022
- 资助金额:
$ 18.6万 - 项目类别:
Standard Grant
Intergovernmental Personnel Act (IPA) with U of Utah - Manish Parashar partial 3rd year and full 4th year continuation (2021-2022)
与犹他大学的政府间人事法 (IPA) - Manish Parashar 部分第三年和完整第四年延续(2021-2022)
- 批准号:
2112830 - 财政年份:2021
- 资助金额:
$ 18.6万 - 项目类别:
Intergovernmental Personnel Award
EAGER: Exploring Federations of Campus and National Cyberinfrastructure as Scalable Platforms for Science: A Case Study using Open Science Grid
EAGER:探索校园联盟和国家网络基础设施作为可扩展的科学平台:使用开放科学网格的案例研究
- 批准号:
1441376 - 财政年份:2014
- 资助金额:
$ 18.6万 - 项目类别:
Standard Grant
II-NEW: An Experimental Platform for Investigating Energy-Performance Tradeoffs for Systems with Deep Memory Hierarchies
II-新:用于研究具有深度内存层次结构的系统的能源性能权衡的实验平台
- 批准号:
1305375 - 财政年份:2013
- 资助金额:
$ 18.6万 - 项目类别:
Standard Grant
Scalable Data Coupling Abstraction for Data-Intensive Simulation Workflows
数据密集型仿真工作流程的可扩展数据耦合抽象
- 批准号:
1310283 - 财政年份:2013
- 资助金额:
$ 18.6万 - 项目类别:
Standard Grant
Exploring Cloud Paradigm and Practices for Science and Engineering
探索科学与工程的云范式和实践
- 批准号:
1339036 - 财政年份:2013
- 资助金额:
$ 18.6万 - 项目类别:
Standard Grant
Collaborative Research: Software Infrastructure for Accelerating Grand Challenge Science with Future Computing Platforms
协作研究:利用未来计算平台加速重大挑战科学的软件基础设施
- 批准号:
1216696 - 财政年份:2012
- 资助金额:
$ 18.6万 - 项目类别:
Standard Grant
Collaborative Research: Error Estimation, Data Assimilation and Uncertainty Quantification for Multiphysics and Multiscale Processes in Geological Media
合作研究:地质介质中多物理场和多尺度过程的误差估计、数据同化和不确定性量化
- 批准号:
1228203 - 财政年份:2012
- 资助金额:
$ 18.6万 - 项目类别:
Standard Grant
US/India Workshop on Virtual Institutes for Computational and Data-Enabled Science & Engineering
美国/印度计算和数据科学虚拟研究所研讨会
- 批准号:
1210266 - 财政年份:2012
- 资助金额:
$ 18.6万 - 项目类别:
Standard Grant
CDI-Type II: Collaborative Research: Computational Models for Evaluating Long Term CO2 Storage in Saline Aquifers
CDI-Type II:合作研究:评估咸水层长期二氧化碳封存的计算模型
- 批准号:
0835436 - 财政年份:2008
- 资助金额:
$ 18.6万 - 项目类别:
Standard Grant
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